Agent skill

Prediction Market Live Ops

by agiprolabs in agiprolabs/claude-trading-skills

A skill your agent uses when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and…

MITAuto-check passedBusiness, Finance & HR

Install Prediction Market Live Ops

skills CLI
$ npx skills add agiprolabs/claude-trading-skills --skill prediction-market-live-ops -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agiprolabs/claude-trading-skills prediction-market-live-ops --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prediction-market-live-ops .claude/skills/prediction-market-live-ops && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prediction-market-live-ops
GitHub stars
410
Token cost
~1.5k tokens
SKILL.md length
775 words
Files
1
Skills in repo
68
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and…

  • Scaling automated trading on prediction markets (Kalshi
  • SKILL.md covers The prime directive, Backtest hygiene (each rule…, Live execution rules (the… and Scaling doctrine, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Similar) — evidence-gated methodology

What it does

Prediction Market Live Ops is an agent skill from agiprolabs/claude-trading-skills. Use when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and falsification protocols distilled from a real live-money campaign

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Trading and backtesting and Backend development. It works with Kalshi. The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.

When your agent uses it

  • Scaling automated trading on prediction markets (Kalshi
  • Similar) — evidence-gated methodology
  • Live-execution safety rails
  • Falsification protocols distilled from a real live-money campaign

Example prompts

  • “/prediction-market-live-ops”

What it can do on your machine

Read from SKILL.md and the folder at commit 981e1d7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prediction Market Live Ops loads about 1.5k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 775 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 775 words, ~1,537 tokens.

Download SKILL.mdSave it as .claude/skills/prediction-market-live-ops/SKILL.md (or your agent's skills folder).
name
prediction-market-live-ops
description
Use when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and falsification protocols distilled from a real live-money campaign

Prediction-Market Trading Doctrine

Hard-won operational knowledge from a live Kalshi campaign (Aug–Sep 2026: crypto/commodity maker ladders, regime tilts, tennis dust vacuum — all falsified honestly, infrastructure open-sourced). Reference implementation: https://github.com/agiprolabs/kalshi-stack. Complements prediction-market-strategy (edge selection/sizing) — this skill covers the OPERATIONAL side: validating, executing, and killing strategies against a live venue without losing more than the lesson costs.

The prime directive

Every strategy decision is evidence-gated: backtest → paper → live micro-probe → ratchet. Each stage has a PRE-DECLARED kill condition with correctly calibrated probability math, written down BEFORE the data arrives. When a threshold turns out miscalibrated, correct it openly and re-declare — never silently move a goalpost in either direction.

Backtest hygiene (each rule was violated once, expensively)

  • Print-replay EV of passive-fill strategies is an UPPER BOUND, never an estimate. The prints a resting order actually captures are an adversely-selected sample of the tape (sweeps deep enough to reach your queue skew toward true collapses). A tennis dust book that measured +125%/$ on prints went 0-for-78 live (P<2%). Only fill-conditioned live outcomes validate a maker strategy.
  • Time-series joins use the last bar ending ≤ the decision instant. A candle containing the instant is look-ahead and will fabricate covariates that vanish out-of-sample.
  • Taker backtests need print-confirmed executability, not quoted-ask edges — stale quotes create phantom fills.
  • Any bucketed-PnL review includes a top-N-share column. Lottery-payoff books concentrate EV; a "pattern" that is two elephant rounds is noise. Same rule when a holdout "wins": check concentration before believing it.
  • Early settlements are selection-biased toward losers (collapses end fast; comebacks are long matches/rounds). Never read a verdict off the fast-settling prefix.
  • Static tilts that flip sign between tape halves are regime bets, not edges. A conditioning scheme must beat baseline on BOTH halves and on each individual day; pre-register the rules, no per-day fitting.
  • Mirror/complement markets are the SAME bet — deduplicate before computing independence-based probabilities.

Live execution rules (the incident ledger)

  • Smoke-test every live order path with a real 1-lot round trip (place, verify resting, cancel, verify gone) before any strategy trades through it. Paper cannot exercise venue routing parameters. Cost: $0.01.
  • Cancels must be truthful: distinguish cancelled / already-gone (filled!) / failed. A 404 is not a cancel — it means wrong routing shard or a fill-race. Swallowed cancel failures + re-anchor loops = runaway position stacking (cost us $84 in one evening).
  • Route the exchange shard (exchange_index) consistently on EVERY call — placement, reads, cancels, collateral. Collateral must be pre-positioned on the shard before orders.
  • Measure rate limits empirically with dust orders (burst ladder + sustained test). Advertised budgets can be 10× off effective per-order cost. Pace bursts to the measured rate with retry-requeue on 429.
  • post_only for any maker strategy — an "aggressive maker" price on a market trading inside your band silently becomes a taker fill of a different (unvalidated) trade. Tag and cohort maker vs taker fills separately in all realized-EV accounting.
  • Rails, always: halt-file checked before every order batch (cancel-all on appearance), independent scheduler-driven drawdown breaker with deposit-jump detection, per-order and per-market size caps with anomaly ledger events, equity floor stand-down. The breaker baseline is set at session start per the operator's stated loss tolerance.
  • Queue priority is the product in penny/maker books. Synchronized-open venues: NTP + fire at boundary+0 with a retry probe (measure the venue's open-transition latency). Event-driven venues: push-based discovery (lifecycle WebSocket channels) beats polling; a fresh market's book is empty, so first-to-rest owns the level for its lifetime.
  • Restart daemons at activity boundaries; mid-round restarts orphan in-memory state (untracked orders, missed settlements).
  • Geofencing is real: order-origin IP matters, categories differ (sports/elections vs financials), and enforcement can change overnight. Datacenter location is part of your compliance posture.
Show full SKILL.md (179 more words)Show less

Scaling doctrine

  • House-money ratchet: double size only when realized profit at the current step covers the next step's incremental risk AND round-count and EV-continuity gates pass. Drop back a step on trailing degradation.
  • Measure the capacity curve (EV/$ by per-print cap) before believing any scale projection; then the binding constraint is capture rate × measured pool, and capture rate is only measurable live.
  • Fill quality is the untested link at every new size — the paper/live EV-per-$ ratio at each step gates the next.

Experiment operations

  • Cohort every fill (maker/taker, band, source) at write time — post-hoc attribution is what lets a bug contaminate a verdict.
  • Ship one variable at a time; changing size and placement logic in the same window destroys attribution.
  • Run A/B variants in paper against a frozen baseline (small-footprint variant, shared rounds, EV/premium-$ as the scale-free judge).
  • Log everything to append-only ledgers; reconcile realized PnL to the venue's own fills/settlements, never to internal marks.
  • Post results (wins AND falsifications) to the operator's channel; write a decision record with evidence and reversal conditions for every ship, retire, and kill.

© agiprolabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/prediction-market-live-ops of agiprolabs/claude-trading-skills.

Open the folder on GitHubat commit 981e1d7

Compare with similar skills

Prediction Market Live Ops next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Prediction Market Live Ops compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prediction Market Live Ops this skillagiprolabs/claude-trading-skills410—~1.5kAutomated safety check: PassMIT
Digital Oraclekomako-workshop/digital-oracle878—~5.9kAutomated safety check: PassMIT
Dr Manhattanguzus/dr-manhattan204—~2kAutomated safety check: PassApache-2.0
Kalshi Traderyanfrigo/kalshi-ai-trading-bot613—~3.4kAutomated safety check: PassMIT
Polymarket Tennislivetennisapi/livetennisapi-mcp152—~3kAutomated safety check: PassMIT
Trading Kalshialsk1992/CloddsBot3k—~856Automated safety check: PassMIT

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Works with

Questions about Prediction Market Live Ops

What does Prediction Market Live Ops do?

A skill your agent uses when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and…. Prediction Market Live Ops is an agent skill from agiprolabs/claude-trading-skills.

When should I use Prediction Market Live Ops?

Prediction Market Live Ops fits situations like: scaling automated trading on prediction markets (Kalshi; similar) — evidence-gated methodology; live-execution safety rails; falsification protocols distilled from a real live-money campaign.

How do I install Prediction Market Live Ops in Claude Code?

Run `npx skills add agiprolabs/claude-trading-skills --skill prediction-market-live-ops -a claude-code`. Or copy the skill folder (skills/prediction-market-live-ops in agiprolabs/claude-trading-skills) into .claude/skills/prediction-market-live-ops in your project. Claude Code loads it when a task matches its description.

How do I install Prediction Market Live Ops in Codex?

Run `npx skills add agiprolabs/claude-trading-skills --skill prediction-market-live-ops -a codex`. Or copy the skill folder (skills/prediction-market-live-ops in agiprolabs/claude-trading-skills) into .agents/skills/prediction-market-live-ops in your project. Codex loads it when a task matches its description.

Can I use Prediction Market Live Ops in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agiprolabs/claude-trading-skills --skill prediction-market-live-ops -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prediction-market-live-ops, .gemini/skills/prediction-market-live-ops, .github/skills/prediction-market-live-ops and .opencode/skills/prediction-market-live-ops in your project.

What does Prediction Market Live Ops need to run?

SKILL.md names no scripts, command-line tools or credentials: Prediction Market Live Ops is instructions for the agent only.

Does Prediction Market Live Ops access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Prediction Market Live Ops safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Prediction Market Live Ops use?

Prediction Market Live Ops is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prediction Market Live Ops use?

About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Prediction Market Live Ops?

Skills that share tags, products or a category with Prediction Market Live Ops: Digital Oracle (komako-workshop/digital-oracle, 878 stars), Dr Manhattan (guzus/dr-manhattan, 204 stars), Kalshi Trade (ryanfrigo/kalshi-ai-trading-bot, 613 stars) and Polymarket Tennis (livetennisapi/livetennisapi-mcp, 152 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prediction Market Live Ops?

agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.

Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.